@article \{st0440,
author = "Cro, S.",
author = "Morris, T. P.",
author = "Kenward, M. G.",
author = "Carpenter, J. R.",
title = "Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation",
journal = "Stata Journal",
publisher = "Stata Press",
address = "College Station, TX",
volume = "16",
number = "2",
year = "2016",
abstract = "Randomized controlled trials provide essential evidence for the evaluation of
new and existing medical treatments. Unfortunately, the statistical analysis is
often complicated by the occurrence of protocol deviations, which mean we
cannot always measure the intended outcomes for individuals who deviate,
resulting in a missing\endashdata problem. In such settings, however one approaches
the analysis, an untestable assumption about the distribution of the unobserved
data must be made. To understand how far the results depend on these
assumptions, the primary analysis should be supplemented by a range of
sensitivity analyses, which explore how the conclusions vary over a range of
different credible assumptions for the missing data. In this article, we
describe a new command, mimix, that can be used to perform
reference\endashbased sensitivity analyses for randomized controlled trials with
longitudinal quantitative outcome data, using the approach proposed by
Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical
Statistics 23: 1352\endash1371). Under this approach, we make qualitative
assumptions about how individuals’ missing outcomes relate to those observed in
relevant groups in the trial, based on plausible clinical scenarios.
Statistical analysis then proceeds using the method of multiple imputation.",
pages = "443-463(21)",
url = "http://www.stata-journal.com/article.html?article=st0440"
}